Magnetic resonance imaging (MRI) is commonly used to image the internal tissues of a subject.
MRI is typically performed by placing the subject or object to be imaged at or near the isocenter of a strong, uniform magnetic field, B0, known as the main magnetic field. The main magnetic field causes the atomic nuclei (spins) that possess a magnetic moment in the matter comprising the subject or object to become aligned in the magnetic field. The spins form a magnetization that processes around the magnetic field direction at a rate proportional to the magnetic field strength. For hydrogen nuclei (which are the common nuclei employed in MRI), the precession frequency is approximately 64 MHz in a magnetic field of 1.5 Tesla. If the magnetization is perturbed by a small radio-frequency magnetic field, known as a B1 magnetic field, the spins emit radiation at a characteristic radio frequency (RF). The emitted RF radiation can be detected and analyzed to yield information that may be used to produce an image of the subject or object. For purposes of the discussion herein, the term “object” will be used to refer to either a subject (e.g., a person) or an object (e.g., a test object) when describing magnetic resonance imaging of that “object.”
In practice, magnetic field gradients are also applied to the subject or object in addition to the main magnetic field. The field gradients are typically applied along one or more orthogonal axes, (x, y, z), the z-axis usually being aligned with the B0, and introduce spatially-distributed variations in frequency and/or phase of the processing nuclear spins. By applying the radio-frequency B1 magnetic field and gradient fields in carefully devised pulses and/or sequences of pulses that are switched on and off, the RF radiation emitted can carry spatially encoded information that, when detected and analyzed, can be used to produce detailed, high resolution images of the subject or object. Various techniques utilizing both specific pulse sequences and advanced image reconstruction methods have been developed, providing new advances, as well as introducing new challenges.
An MRI system typically includes hardware components, including a plurality of gradient coils positioned about a bore of a magnet, an RF transceiver system, and an RF switch controlled by a pulse module to transmit RF signals to and receive RF signals from an RF coil assembly. The received RF signals are also known as magnetic resonance (MR) signal data. An MRI system also typically includes a computer programmed to cause the system to apply to an object in the system various RF signals, magnetic fields, and field gradients for inducing spin excitations and spatial encoding in an object, to acquire MR signal data from the object, to process the MR signal data, and to construct an MR image of the object from the processed MR signal data. The computer can include one or more general or special purpose processors, one or more forms of memory, and one or more hardware and/or software interfaces for interacting with and/or controlling other hardware components of the MRI system.
MR signal data detected from an object are typically described in mathematical terms as “k-space” data (k-space is the 2D Fourier transform of the image). An image in actual space is produced by a Fourier transform of the k-space data. MR signal data are acquired by traversing k-space over the course of applying to the object the various RF pulses and magnetic field gradients. In practice, techniques for acquiring MR signal data from an object are closely related to techniques for applying the various RF pulses and magnetic field gradients to the object.
In diffusion weighted MRI (DWI) studies of neural tissue (e.g., human or other animal brain), a classical model assumes the statistical mechanics of Brownian motion and predicts a mono-exponential signal decay. However, some studies indicate signal decays which are not mono-exponential, particularly in the white matter. This deviation from the mono-exponential decay regime is called “anomalous diffusion.” In neurodegenerative diseases, neural injuries, and neuroblastomas, the tissue microstructure in the brain is found to be altered from that of healthy neural tissue. In principle, DWI offers the non-invasive monitoring capability to measure microstructural changes via diffusion dynamics of water within the probed tissue to quantify the pathology.
In conventional DWI, a signal is modeled as the mono-exponential decay function, exp[−(bD)], where D is a classical diffusion coefficient and b is a MRI pulse sequence controlled parameter comprised of q (i.e., diffusion spatial resolution) and A (i.e., diffusion temporal resolution). To interpret the anomalous diffusion decay signal which is not mono-exponential, conventional techniques have been modified by applying: (1) a bi-exponential function, (e.g. Aexp[−bDi]+(1−A)exp[−bD2] where Δ is a volume fraction, D1 is the fast diffusion coefficient, and D2 is the slow diffusion coefficient); (2) a stretched exponential function (e.g., exp[−(bD)w], where w is the stretching parameter over the entire b value; (3) a stretched exponential function (e.g., exp[−(qwΔD)], where w is the stretching parameter over the b-value component, q; and (4) a stretched exponential function (e.g., exp[−(qΔw D)], where w is the stretching parameter over the b-value component, A.
Accordingly a need exists for a DWI protocol that is able to resolve diffusion patterns in neural tissue morphology, and for a comprehensive methodology for interpreting MRI anomalous diffusion decay signals.